rm(list = ls())
knitr::opts_chunk$set(echo = TRUE,
                      message = FALSE,
                      warning = FALSE,
                      fig.align = 'center',
                      dev = 'jpeg',
                      dpi = 300, 
                      fig.align='center')
#XQuartz is a mess, put this in your onload to default to cairo instead
options(bitmapType = "cairo") 
# (https://github.com/tidyverse/ggplot2/issues/2655)
# Lo mapas se hacen mas rapido

library(tidyverse)
library(ggridges)
library(readxl)
library(here)
library(lubridate)
library(readr)
library(ggthemes)
library(hrbrthemes)
library(viridis)
library(kableExtra)
library(ggalt)

1 OBJETIVO

The following document and code intends to carry out a complementary methodological Exploratory Data Analysis from survey data in coquina (Donux truculus) in a historic context review of FEMP_AND_04 project.

In this case, we analysed biological component like lengths structure, density indicator and fishery yield in CPUE type.

This analysis are essential to give advice to Junta de Andaluacía through management plan to D. trunculus (Agricultura & Rural, 2023).

2 AREA DE ESTUDIO

La zona de distribución de la coquina objeto de este análisis es en base a la aplicación de la regulación marisquera española, relacionado con la producción. Para ello, el litoral andaluz se dividió en diferentes zonas de producción (ZZPP) las cuales se encuentran definidas en la Orden de 15 de julio de 1993 (BOJA nº 85 de 5/8/1993).

En esta Orden se declaran las zonas de producción y protección o mejora de moluscos bivalvos, moluscos gasterópodos, tunicados y equinodermos marinos de la Comunidad Autónoma de Andalucía, fuera de las cuales quedará prohibida la su recolección. Esta norma delimita zonas de producción de moluscos bivalvos a lo largo del litoral andaluz en los cuales se encuentran los puntos de muestreo establecidos en el seguimiento temporal de D. trunculus en el litoral de Huelva llevado a cabo por el IEO (Marco & Delgado, 2022) (Figura 2.1).

Figure 2.1: Mapa con los puntos de muestreo establecidos en el seguimiento temporal de D. trunculus en el litoral de Huelva llevado a cabo por el IEO.

3 ENFOQUE DE AED

These data, spetially length frecuencies, must be weighted to the sampling estimates, because they are just a subsample. This approach has a logic used to POBLACIONAL (Figura 3.1) and COMERCIAL samples (Figura 3.2);

Figure 3.1: Poblacional sample scheme

Figure 3.2: Comercial sample scheme

En este codigo autocontenido, analizaremos tres componentes de interés. Estructuras de tallas, densidades poblacionales e Indice de reclutamiento.

4 BASES DATOS

En este trabajo se deben revisar todos los componentes que se tienen en cuenta, para ello, investigadores del IEO prepararon una descripción de cada fuente , caracteristicas y su escala temporal. La mayoría de estos dsatos son compuestos por el monitoreo y seguimiento científico de Donax trunculus en el Golfo de Cádiz lque lleva a cabo el IEO y AGAPA.

Item	Periodo	Observación	Agregación
DESEMBARQUE	2020-2022	kg/mariscador o barco/mes	Por playa
ESTRUCTURA TALLAS	2017-2023	Datos previos al 2020 deben ser revisados	Por playa, por tipo de rastro
vB Linf		46 mm	Revisar
M		M=2k	Revisar
vB k		0.48	Revisar
EDAD MÁXIMA		EM= log(0.01)/M	Revisar
Parámetros gravimetricos		a;b	Revisar
DENSIDAD	2017-2023	g/m2/	Mes, Playa, Rastro
RENDIMIENTO (CPUE)	2018-2023	3 horas/mariscador/dia. (180minpeso coquina>25mm5min)	Por Mes, playa, rastro
INDICE RECLUTAMIENTO (D15)	2017-2022	ind/m2 < 15mm	Por Mes, playa, rastro
TALLA PRIMERA MADUREZ		L50=10.8mm	L95= pendiente

En cuanto a aspectos reproductivos, la reproducción de coquina es entre los meses de Febrero – julio, con un maximo de desove entre mayo- julio, coincidiendo con la veda (Agricultura & Rural, 2023).

4.1 Leer y juntar Data Base

4.1.1 Bases de Longitudes

# Datos 2020 size and dens and abundance join
size2017 <- read.csv2(here("Data", "Anterior a 2020", "data_ieo_2017_def.csv"), dec=".")
size2018 <- read.csv2(here("Data", "Anterior a 2020", "data_ieo_2018_def.csv"), dec=".")
size2019 <- read.csv2(here("Data", "Anterior a 2020", "data_ieo_2019_def.csv"), dec=".")
size2020 <- read.csv2(here("Data", "Anterior a 2020", "data_ieo_2020_def.csv"), dec=".")

# datos post 2020 separate files sizes and dens

# Lenght 
size2021 <- read_excel(here("Data", "Posterior 2020", "Data_size_Coquina_2021.xlsx"), 
                       sheet = "Coquina_donax")
size2022 <- read_excel(here("Data", "Posterior 2020", "Data_size_Coquina_2022.xlsx"),  
                       sheet = "Coquina_donax")
size2023 <- read_excel(here("Data", "Posterior 2020", "Data_size_Coquina_2023.xlsx"),  
                       sheet = "Coquina_Donax")

4.1.2 Bases de Densidades

Se deben leer las dos hojas POBLACIONAL y COMERCIAL por separado y luego unir.

Recordar wque las bases de densidades previas al 2020 estan en la misma base que las longitudes

dens2021pob <- read_excel(here("Data", "Posterior 2020", 
                               "Data_sample_FEMP_04_2021.xlsx"),
                       sheet = "Data_POBL")
dens2021com <- read_excel(here("Data", "Posterior 2020", 
                               "Data_sample_FEMP_04_2021.xlsx"),
                       sheet = "DATA_COM")
dens2022pob <- read_excel(here("Data", "Posterior 2020", 
                               "Data_sample_FEMP_04_2022.xlsx"),
                       sheet = "Data_POBL")
dens2022com <- read_excel(here("Data", "Posterior 2020", 
                               "Data_sample_FEMP_04_2022.xlsx"),
                       sheet = "DATA_COM")
dens2023pob <- read_excel(here("Data", "Posterior 2020", 
                               "Data_sample_FEMP_04_2023.xlsx"),
                       sheet = "Data_POBL")
dens2023com <- read_excel(here("Data", "Posterior 2020", 
                               "Data_sample_FEMP_04_2023.xlsx"),
                       sheet = "DATA_COM")

5 COMPOSICIONES DE TALLAS

Este aspecto se trabaja de forma de ponderación ad-hoc descrita en la Figure 3.1

5.1 Test dimension and names columns and diferences

dim(size2017)

## [1] 10121    28

dim(size2018)

## [1] 20418    28

dim(size2019)

## [1] 18109    28

dim(size2020)

## [1] 13435    28

dim(size2021)

## [1] 21971    12

names(size2017)

##  [1] "months"                      "Date"                       
##  [3] "Beach"                       "Sampling.point"             
##  [5] "track_activelog"             "lat_1"                      
##  [7] "long_1"                      "lat_2"                      
##  [9] "long_2"                      "plus_m"                     
## [11] "tow_time"                    "rastro"                     
## [13] "zaranda"                     "mariscador"                 
## [15] "sample"                      "Sample_weight"              
## [17] "Clam_sample_weigth"          "Measured_clam_sample_weigth"
## [19] "CAT"                         "Categoria"                  
## [21] "Size"                        "SizeE"                      
## [23] "Tide_coef"                   "Low_tide_hour"              
## [25] "Sampling_hour"               "number_fisherman"           
## [27] "veda"                        "dists"

names(size2018)

##  [1] "months"                      "Date"                       
##  [3] "Beach"                       "Sampling.point"             
##  [5] "track_activelog"             "lat_1"                      
##  [7] "long_1"                      "lat_2"                      
##  [9] "long_2"                      "plus_m"                     
## [11] "tow_time"                    "rastro"                     
## [13] "zaranda"                     "mariscador"                 
## [15] "sample"                      "Sample_weight"              
## [17] "Clam_sample_weigth"          "Measured_clam_sample_weigth"
## [19] "CAT"                         "Categoria"                  
## [21] "Size"                        "SizeE"                      
## [23] "Tide_coef"                   "Low_tide_hour"              
## [25] "Sampling_hour"               "number_fisherman"           
## [27] "veda"                        "dists"

names(size2019)

##  [1] "months"                      "Date"                       
##  [3] "Beach"                       "Sampling.point"             
##  [5] "track_activelog"             "lat_1"                      
##  [7] "long_1"                      "lat_2"                      
##  [9] "long_2"                      "plus_m"                     
## [11] "tow_time"                    "rastro"                     
## [13] "zaranda"                     "mariscador"                 
## [15] "sample"                      "Sample_weight"              
## [17] "Clam_sample_weigth"          "Measured_clam_sample_weigth"
## [19] "CAT"                         "Categoria"                  
## [21] "Size"                        "SizeE"                      
## [23] "Tide_coef"                   "Low_tide_hour"              
## [25] "Sampling_hour"               "number_fisherman"           
## [27] "veda"                        "dists"

names(size2020)

##  [1] "months"                      "Date"                       
##  [3] "Beach"                       "Sampling.point"             
##  [5] "track_activelog"             "lat_1"                      
##  [7] "long_1"                      "lat_2"                      
##  [9] "long_2"                      "plus_m"                     
## [11] "tow_time"                    "rastro"                     
## [13] "zaranda"                     "mariscador"                 
## [15] "sample"                      "Sample_weight"              
## [17] "Clam_sample_weigth"          "Measured_clam_sample_weigth"
## [19] "CAT"                         "Categoria"                  
## [21] "Size"                        "SizeE"                      
## [23] "Tide_coef"                   "Low_tide_hour"              
## [25] "Sampling_hour"               "number_fisherman"           
## [27] "veda"                        "dists"

names(size2021)

##  [1] "species"                "Date"                   "Beach"                 
##  [4] "Sampling.point"         "rastro"                 "CAT"                   
##  [7] "Categoria"              "size"                   "sizeE"                 
## [10] "ID"                     "ID_codificado_punto"    "ID_codificado_muestreo"

Same names. Could merge the DF

size_17_20 <- rbind(size2017,
                    size2018,
                    size2019,
                    size2020)
# new dimension
dim(size_17_20)

## [1] 62083    28

names(size_17_20)

##  [1] "months"                      "Date"                       
##  [3] "Beach"                       "Sampling.point"             
##  [5] "track_activelog"             "lat_1"                      
##  [7] "long_1"                      "lat_2"                      
##  [9] "long_2"                      "plus_m"                     
## [11] "tow_time"                    "rastro"                     
## [13] "zaranda"                     "mariscador"                 
## [15] "sample"                      "Sample_weight"              
## [17] "Clam_sample_weigth"          "Measured_clam_sample_weigth"
## [19] "CAT"                         "Categoria"                  
## [21] "Size"                        "SizeE"                      
## [23] "Tide_coef"                   "Low_tide_hour"              
## [25] "Sampling_hour"               "number_fisherman"           
## [27] "veda"                        "dists"

glimpse(size_17_20)

## Rows: 62,083
## Columns: 28
## $ months                      <int> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, …
## $ Date                        <chr> "13/07/2017", "13/07/2017", "13/07/2017", …
## $ Beach                       <chr> "Donana", "Donana", "Donana", "Donana", "D…
## $ Sampling.point              <chr> "2", "2", "2", "2", "2", "2", "2", "2", "2…
## $ track_activelog             <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ lat_1                       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ long_1                      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ lat_2                       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ long_2                      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ plus_m                      <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tow_time                    <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, …
## $ rastro                      <chr> "COMERCIAL", "COMERCIAL", "COMERCIAL", "CO…
## $ zaranda                     <chr> "R", "R", "R", "R", "R", "R", "R", "R", "R…
## $ mariscador                  <chr> "LUIS", "LUIS", "LUIS", "LUIS", "LUIS", "L…
## $ sample                      <chr> "13/07/2017", "13/07/2017", "13/07/2017", …
## $ Sample_weight               <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ Clam_sample_weigth          <dbl> 195, 195, 195, 195, 195, 195, 195, 195, 19…
## $ Measured_clam_sample_weigth <dbl> 195, 195, 195, 195, 195, 195, 195, 195, 19…
## $ CAT                         <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ Categoria                   <chr> "", "", "", "", "", "", "", "", "", "", ""…
## $ Size                        <dbl> 27.21, 26.65, 26.65, 25.07, 27.49, 26.15, …
## $ SizeE                       <int> 27, 26, 26, 25, 27, 26, 26, 28, 25, 28, 26…
## $ Tide_coef                   <int> 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72…
## $ Low_tide_hour               <chr> "12:30 AM", "12:30 AM", "12:30 AM", "12:30…
## $ Sampling_hour               <chr> "", "", "", "", "", "", "", "", "", "", ""…
## $ number_fisherman            <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ veda                        <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dists                       <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…

5.2 Change `Date` columns from `character`to `Date` format

size_17_20$Date <- dmy(size_17_20$Date)
# separo los meses , dias y años
# Separar en columnas de día, mes y año
realdate <- as.Date(size_17_20$Date, format="%Y-%M-%D")

dfdate <- data.frame(Date=realdate)
ANO=as.numeric (format(realdate,"%Y"))
MES=as.numeric (format(realdate,"%m"))
DIA=as.numeric (format(realdate,"%d"))

size2<-cbind(dfdate,ANO,MES,DIA,size_17_20)
colnames(size2)

##  [1] "Date"                        "ANO"                        
##  [3] "MES"                         "DIA"                        
##  [5] "months"                      "Date"                       
##  [7] "Beach"                       "Sampling.point"             
##  [9] "track_activelog"             "lat_1"                      
## [11] "long_1"                      "lat_2"                      
## [13] "long_2"                      "plus_m"                     
## [15] "tow_time"                    "rastro"                     
## [17] "zaranda"                     "mariscador"                 
## [19] "sample"                      "Sample_weight"              
## [21] "Clam_sample_weigth"          "Measured_clam_sample_weigth"
## [23] "CAT"                         "Categoria"                  
## [25] "Size"                        "SizeE"                      
## [27] "Tide_coef"                   "Low_tide_hour"              
## [29] "Sampling_hour"               "number_fisherman"           
## [31] "veda"                        "dists"

table(size2$ANO)

## 
##  2017  2018  2019  2020 
## 10121 20418 18109 13435

Now we test.

table(size2$ANO)

## 
##  2017  2018  2019  2020 
## 10121 20418 18109 13435

5.3 Viz

Primera vizulación de las tallas de coquina diferenciasdas por tipo de muestreo. Línea roja es SL50 (10.8 mm para hembras (Delgado et al., 2017) y línea amarilla es la talla mínima de extracción legal en 25 mm. (Delgado & Silva, 2018).

nreg <- ggplot(size2 %>% 
                 select(-1), 
               aes(x=Size, 
                   y = as.factor(MES),
                  fill= as.factor(rastro)))+
  geom_density_ridges(stat = "binline", 
                      bins = 40, 
                      scale = 1.2,
                      alpha=0.7)+
  facet_wrap(.~ANO, ncol=4) +
  geom_vline(xintercept = 10.8, color = "red")+
  geom_vline(xintercept = 25, color = "yellow")+
  scale_fill_manual(values = c("#636363", "#2c7fb8", "#de2d26", "#756bb1", "#2ca25f"),
                       name="Rastro")+
  theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme_few()+
  xlab("Longitud (cm.)")+
  ylab("")+
  xlim(0,40)
#scale_x_discrete((limits = rev(levels(talla2021$ANO_ARR))))+
nreg

by beach

nbeach <- ggplot(size2 %>% 
                 select(-1), 
               aes(x=SizeE, 
                   y = as.factor(MES),
                  fill= as.factor(Beach)))+
  geom_density_ridges(stat = "binline", 
                      bins = 40, 
                      scale = 1.2,
                      alpha=0.7)+
  facet_wrap(.~ANO, ncol=4) +
  geom_vline(xintercept = 10.8, color = "red")+
  scale_fill_viridis_d(option="F",
                       name="Beach")+
  theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme_few()+
  xlab("Longitud (cm.)")+
  ylab("")+
  xlim(0,40)
#scale_x_discrete((limits = rev(levels(talla2021$ANO_ARR))))+
nbeach

Now, we handling data 2021-2023. Same columns data 2017-2020

size2021b <- size2021 %>% 
  select(2, 3, 4, 5, 6, 7, 8, 9, 12)
names(size2021b)

## [1] "Date"                   "Beach"                  "Sampling.point"        
## [4] "rastro"                 "CAT"                    "Categoria"             
## [7] "size"                   "sizeE"                  "ID_codificado_muestreo"

size2022b <- size2022 %>% 
  select(-c(1, 2))
size2023b <- size2023 %>% 
  select(-c(1, 2))

size_21_23 <- rbind(size2021b,
                    size2022b,
                    size2023b)

5.4 Separate `Date` column

# separo los meses , dias y años
# Separar en columnas de día, mes y año
realdate2 <- as.Date(size_21_23$Date, format="%Y-%M-%D")

dfdate2 <- data.frame(Date=realdate2)
ANO=as.numeric (format(realdate2,"%Y"))
MES=as.numeric (format(realdate2,"%m"))
DIA=as.numeric (format(realdate2,"%d"))

size3<-cbind(dfdate2,ANO,MES,DIA,size_21_23)
colnames(size3)

##  [1] "Date"                   "ANO"                    "MES"                   
##  [4] "DIA"                    "Date"                   "Beach"                 
##  [7] "Sampling.point"         "rastro"                 "CAT"                   
## [10] "Categoria"              "size"                   "sizeE"                 
## [13] "ID_codificado_muestreo"

table(size3$ANO)

## 
##  2021  2022  2023 
## 21971 17426  6751

Now join all years

names(size2) # 2017-2020

##  [1] "Date"                        "ANO"                        
##  [3] "MES"                         "DIA"                        
##  [5] "months"                      "Date"                       
##  [7] "Beach"                       "Sampling.point"             
##  [9] "track_activelog"             "lat_1"                      
## [11] "long_1"                      "lat_2"                      
## [13] "long_2"                      "plus_m"                     
## [15] "tow_time"                    "rastro"                     
## [17] "zaranda"                     "mariscador"                 
## [19] "sample"                      "Sample_weight"              
## [21] "Clam_sample_weigth"          "Measured_clam_sample_weigth"
## [23] "CAT"                         "Categoria"                  
## [25] "Size"                        "SizeE"                      
## [27] "Tide_coef"                   "Low_tide_hour"              
## [29] "Sampling_hour"               "number_fisherman"           
## [31] "veda"                        "dists"

names(size3)# 2021-2023

##  [1] "Date"                   "ANO"                    "MES"                   
##  [4] "DIA"                    "Date"                   "Beach"                 
##  [7] "Sampling.point"         "rastro"                 "CAT"                   
## [10] "Categoria"              "size"                   "sizeE"                 
## [13] "ID_codificado_muestreo"

size2fil <- size2 %>% 
  select(1, 2, 3, 4, 7, 8, 16, 23, 24, 25, 26)
size3fil <- size3 %>% 
  select(-c(13,5)) %>% 
  rename(Size = size,
         SizeE = sizeE)

names(size2fil) # 2017-2020

##  [1] "Date"           "ANO"            "MES"            "DIA"           
##  [5] "Beach"          "Sampling.point" "rastro"         "CAT"           
##  [9] "Categoria"      "Size"           "SizeE"

names(size3fil)# 2021-2023

##  [1] "Date"           "ANO"            "MES"            "DIA"           
##  [5] "Beach"          "Sampling.point" "rastro"         "CAT"           
##  [9] "Categoria"      "Size"           "SizeE"

# join data

sizeall <- rbind(size2fil, size3fil)

check

dim(sizeall)

## [1] 108231     11

table(sizeall$ANO)

## 
##  2017  2018  2019  2020  2021  2022  2023 
## 10121 20418 18109 13435 21971 17426  6751

Rename values

sizeall2 <- sizeall %>% 
  mutate(rastro = str_replace_all(rastro, " ", ""))
unique(sizeall2$rastro)

## [1] "COMERCIAL"    "POBLACIONAL"  "COMERCIALNEW"

some plots

nall <- ggplot(sizeall2, 
               aes(x=Size, 
                   y = as.factor(MES),
                  fill= as.factor(rastro)))+
  geom_density_ridges(stat = "binline", 
                      bins = 50, 
                      scale = 1.2,
                      alpha=0.7)+
  facet_wrap(.~ANO, ncol=7) +
  geom_vline(xintercept = 10.8, color = "red")+
  scale_fill_viridis_d(option="B",
                       name="Rastro")+
  theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme_few()+
  theme(legend.position = "bottom")+
  xlab("Longitud (cm.)")+
  ylab("")+
  xlim(0,40)
#scale_x_discrete((limits = rev(levels(talla2021$ANO_ARR))))+
nall

nallbeach <- ggplot(sizeall2, 
               aes(x=Size, 
                   y = as.factor(MES),
                  fill= as.factor(Beach)))+
  geom_density_ridges(stat = "binline", 
                      bins = 50, 
                      scale = 1.2,
                      alpha=0.7)+
  facet_wrap(.~ANO, ncol=7) +
  geom_vline(xintercept = 10.8, color = "red")+
  scale_fill_viridis_d(option="F",
                       name="Beach")+
  theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme_few()+
  theme(legend.position = "bottom")+
  xlab("Longitud (cm.)")+
  ylab("")+
  xlim(0,40)
#scale_x_discrete((limits = rev(levels(talla2021$ANO_ARR))))+
nallbeach

just POBLACIONAL sample

pobeach <- ggplot(sizeall2 %>% 
                      filter(rastro!="COMERCIAL"), 
               aes(x=Size, 
                   y = as.factor(MES),
                  fill= as.factor(Beach)))+
  geom_density_ridges(stat = "binline", 
                      bins = 50, 
                      scale = 1.2,
                      alpha=0.7)+
  facet_wrap(.~ANO, ncol=7) +
  geom_vline(xintercept = 10.8, color = "red")+
  scale_fill_viridis_d(option="G",
                       name="Beach")+
  theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme_few()+
  theme(legend.position = "bottom")+
  xlab("Longitud (cm.)")+
  ylab("")+
  xlim(0,40)
#scale_x_discrete((limits = rev(levels(talla2021$ANO_ARR))))+
pobeach

justm COMERCIAL sample

combeach <- ggplot(sizeall %>% 
                      filter(rastro!="POBLACIONAL"), 
               aes(x=Size, 
                   y = as.factor(MES),
                  fill= as.factor(Beach)))+
  geom_density_ridges(stat = "binline", 
                      bins = 50, 
                      scale = 1.2,
                      alpha=0.7)+
  facet_wrap(.~ANO, ncol=7) +
  geom_vline(xintercept = 10.8, color = "red")+
  scale_fill_viridis_d(option="F",
                       name="Beach")+
  theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme_few()+
  theme(legend.position = "bottom")+
  xlab("Longitud (cm.)")+
  ylab("")+
  xlim(0,40)
#scale_x_discrete((limits = rev(levels(talla2021$ANO_ARR))))+
combeach

last month of 2023 (august) by beach

combeachago23 <- ggplot(sizeall2 %>% 
                      filter(ANO==2023), 
               aes(x=Size, fill=rastro))+
  geom_histogram(bins = 80,
                      alpha=0.8)+
  scale_fill_manual(values = c("red", "blue"))+
  facet_grid(MES~Beach) +
  geom_vline(xintercept = 10.8, color = "red")+
  theme_few()+
  theme(legend.position = "bottom")+
  xlab("Longitud (cm.)")+
  ylab("")+
  xlim(0,40)+
  labs(title= "Survey 2023")
#scale_x_discrete((limits = rev(levels(talla2021$ANO_ARR))))+
combeachago23

another way to viz is

scatter plot

sizemean <-sizeall2 %>% 
  dplyr::group_by(ANO, MES, rastro, Beach) %>%
  dplyr::summarise(avg=mean(SizeE))
#kableExtra::kable(coutlength, format = "html")

Mean length in time series by Subarea.

pmea <- ggplot(sizemean, 
               aes(MES,avg,
               color = factor(Beach)))+
    geom_point(show.legend = T,
               alpha=.7) +
    geom_smooth(method= "lm", 
                colour='#253494')+
    theme_few()+ 
    facet_grid(rastro~ANO)+
    scale_x_continuous(breaks = seq(from = 1, to = 12, by = 3))+
    #scale_y_discrete(breaks = seq(from = 1, to = 13, by = 1))+
    theme(axis.text.x = element_text(angle = 90))+
    guides(fill = guide_legend(reverse=F))+
    scale_color_viridis_d(option="H",
                          name="Beach")+
    ylim(15,30)+
    ylab("") +
    xlab("") +
    ggtitle("Lenght Mean Krill fishery")
pmea

Calculate a recruit index

5.5 Calculate Index Recruit

inderec <- sizeall %>% 
  filter(rastro=="POBLACIONAL") %>% 
  drop_na(Size) %>% 
  dplyr::group_by(ANO, MES) %>% 
  dplyr::mutate(prolen = Size - 10) %>% 
  dplyr::mutate(prolen2 = prolen*-1 ) %>% 
  dplyr::summarize(prolen3 =mean(prolen2))


limite_superior <- round(mean(inderec$prolen3) + 
  1.96 * sd(inderec$prolen3) / sqrt(inderec$prolen3),3)
limite_inferior <- round(mean(inderec$prolen3) - 
  1.96 * sd(inderec$prolen3) / sqrt(inderec$prolen3),3)

inderec$colour <- ifelse(inderec$prolen3 < 0, "negative","positive")

indexplot <- ggplot(inderec,
                    aes(rev(MES),prolen3))+
  geom_bar(stat="identity",
           position="identity",
           aes(fill = colour))+
  scale_fill_manual(values=c(positive="firebrick1",
                             negative="black"),
                    name="")+
  facet_wrap(.~ANO)+
  theme_few()+
  scale_x_continuous(breaks = seq(from = 1, 
                                to = 12, by = 4))+
  labs(y="IRK",
       x="",
       title = "Index Recruit Krill 48.1")+
  coord_flip()
indexplot

6 DENSIDAD DB

7 INDICE DE RECLUTAMIENTO DB

8 YIELD (CPUE) ANALYSIS

9 DESEMBARQUES OFICIALES

Leo los datos entregados por E. Marco. Actualizar pedida con Junta Andalucia.

landings <- read_excel(here("Data", 
                            "Datos_venta_2017_14_02_22.xlsx"))

Identifico las columnas necesarias para el analisis, que en este caso, serían las columnas condato crudo.

# Fecha original en formato "año-mes-día"
fecha_original <- ymd(landings$FECHA_VENTA)
# Separar en año, mes y día
ANO <- year(fecha_original)
MES <- month(fecha_original)
DIA <- day(fecha_original)
# uno la base
landings2 <-cbind(ANO,MES,DIA,landings)

Grafico general de los desembarques

landings3 <- landings2 %>% 
  group_by(ANO, MES, ESTABLECIMIENTO, ZONA_PRODUCCION) %>% 
  summarise(LANDINGS = sum(TOTAL_KILOS)/1000)

hist(landings3$LANDINGS)

quantile(landings3$LANDINGS)

##         0%        25%        50%        75%       100% 
##  0.0010000  0.0500000  0.1908400  0.6449275 14.4210000

Hay valores cercanos a las 14 t. Identificar si esto tiene sentido. Preguntar a MD.

plotlam <- ggplot(landings3,aes(ANO, LANDINGS))+
  geom_bar(stat = "identity")+
  #facet_wrap(.~ESTABLECIMIENTO)+
  theme_few()
plotlam

Otra viz

landpop <- ggplot(landings3 %>% 
         group_by(ANO, ESTABLECIMIENTO) %>% 
         summarise(LANDINGS1 =sum(LANDINGS))) +
  geom_lollipop(aes(x=ANO, 
                  y=LANDINGS1,
                  colour=ESTABLECIMIENTO), 
              size=0.9)+
  scale_colour_viridis_d(option="H")+
  theme_few() +
  theme(
    legend.position = "none",
    panel.border = element_blank(),
    panel.spacing = unit(0.1, "lines"),
    strip.text.x = element_text(size = 6),
    axis.text.x = element_text(size = 5),
    axis.text.y = element_text(size = 5)) +
  xlab("") +
  ylab("Desembarque (t) por Establecimiento") +
  facet_wrap(.~ESTABLECIMIENTO, ncol=4, scale="free_y")
landpop

Los datos fueron solicitados con información hasta Febrero del 2022, por lo mismo es necesario actualizar

orderland <-   ggplot(landings3 %>% 
         group_by(ESTABLECIMIENTO) %>% 
         summarise(LANDINGS1 =sum(LANDINGS)) %>% 
           arrange(LANDINGS1) %>% 
           mutate(ESTABLECIMIENTO=factor(ESTABLECIMIENTO,
                                         ESTABLECIMIENTO)), 
         aes(x=ESTABLECIMIENTO, 
             y=LANDINGS1) ) +
    geom_bar(stat="identity", fill="#69b3a2") +
    coord_flip() +
    theme_ipsum() +
    theme(
      panel.grid.minor.y = element_blank(),
      panel.grid.major.y = element_blank(),
      legend.position="none",
      axis.text.y = element_text(size = 10)
    ) +
    xlab("") +
    ylab("Desembarque total aculumado por Establecimiento")
orderland

kbl(table(landings2$MES, landings2$ANO), 
    longtable = F, 
    booktabs = T, 
    caption = "Desembarque (t) acumulado por Establecimiento") %>% 
   kable_styling(latex_options = c("striped",  "hold_position"))

Table 9.1: Desembarque (t) acumulado por Establecimiento
2017	2018	2019	2020	2021	2022
106	323	269	427	475	463
66	284	302	160	524	225
111	256	327	382	611	0
72	369	340	221	516	0
9	6	69	64	0	0
44	31	299	189	202	0
140	245	472	618	665	0
117	150	301	556	606	0
76	261	144	469	486	0
88	332	330	458	474	0
211	244	226	455	458	0
191	190	292	508	459	0

10 DUDAS

10.1 LFD DB

What is CAT
Difference between size and sizeE
what variable we can see binside MES?
data about maturity and reproductive indicator?
Waypoint by beach?.
How calculate recruit index and another way.
Weigthing LF sensu MD.
como se consigue la estructura luego de ser ponderada?

REFERENCES

Agricultura, D. E., & Rural, D. (2023). Plan de Gestión para la especie Coquina (Donax trunculus) en el Golfo de Cádiz a fin de alcanzar niveles de rendimiento máximo sostenible (pp. 1–16). Junta de Andalucía.

Delgado, M., & Silva, L. (2018). Timing variations and effects of size on the reproductive output of the wedge clam Donax trunculus (L. 1758) in the littoral of Huelva (SW Spain). Journal of the Marine Biological Association of the United Kingdom, 98(2), 341–350. https://doi.org/10.1017/S0025315416001429

Delgado, M., Silva, L., Gómez, S., Masferrer, E., Cojan, M., & Gaspar, M. B. (2017). Population and production parameters of the wedge clam Donax trunculus (Linnaeus, 1758) in intertidal areas on the southwest Spanish coast: Considerations in relation to protected areas. Fisheries Research, 193(April), 232–241. https://doi.org/10.1016/j.fishres.2017.04.012

Marco, E., & Delgado, M. (2022). 4.1 ENTREGABLE 1: INFORME CON LA EVALUACIÓN DE UN CONJUNTO DE MEDIDAS DE GESTIÓN PESQUERA ARMONIZADAS PARA LOS MARISQUEROS QUE OPERAN EN ANDALUCÍA (IEO + IPMA) (pp. 1–25). Instituto Español de Oceanografía, Cádiz.

Historical survey EDA Donux trunculus 2023

Complementary analysis to advice and management report FEMP_AND_04

Mardones, M; Delgado, M

10 October, 2023

1 OBJETIVO

2 AREA DE ESTUDIO

3 ENFOQUE DE AED

4 BASES DATOS

4.1 Leer y juntar Data Base

4.1.1 Bases de Longitudes

4.1.2 Bases de Densidades

5 COMPOSICIONES DE TALLAS

5.1 Test dimension and names columns and diferences

5.2 Change `Date` columns from `character`to `Date` format

5.3 Viz

5.4 Separate `Date` column

5.5 Calculate Index Recruit

6 DENSIDAD DB

7 INDICE DE RECLUTAMIENTO DB

8 YIELD (CPUE) ANALYSIS

9 DESEMBARQUES OFICIALES

10 DUDAS

10.1 LFD DB

REFERENCES

2017	2018	2019	2020	2021	2022
106	323	269	427	475	463
66	284	302	160	524	225
111	256	327	382	611	0
72	369	340	221	516	0
9	6	69	64	0	0
44	31	299	189	202	0
140	245	472	618	665	0
117	150	301	556	606	0
76	261	144	469	486	0
88	332	330	458	474	0
211	244	226	455	458	0
191	190	292	508	459	0

2017	2018	2019	2020	2021	2022
106	323	269	427	475	463
66	284	302	160	524	225
111	256	327	382	611	0
72	369	340	221	516	0
9	6	69	64	0	0
44	31	299	189	202	0
140	245	472	618	665	0
117	150	301	556	606	0
76	261	144	469	486	0
88	332	330	458	474	0
211	244	226	455	458	0
191	190	292	508	459	0

Historical survey EDA Donux trunculus 2023

Complementary analysis to advice and management report FEMP_AND_04

Mardones, M; Delgado, M

10 October, 2023

1 OBJETIVO

2 AREA DE ESTUDIO

3 ENFOQUE DE AED

4 BASES DATOS

4.1 Leer y juntar Data Base

4.1.1 Bases de Longitudes

4.1.2 Bases de Densidades

5 COMPOSICIONES DE TALLAS

5.1 Test dimension and names columns and diferences

5.2 Change Date columns from characterto Date format

5.3 Viz

5.4 Separate Date column

5.5 Calculate Index Recruit

6 DENSIDAD DB

7 INDICE DE RECLUTAMIENTO DB

8 YIELD (CPUE) ANALYSIS

9 DESEMBARQUES OFICIALES

10 DUDAS

10.1 LFD DB

REFERENCES

5.2 Change `Date` columns from `character`to `Date` format

5.4 Separate `Date` column

2017	2018	2019	2020	2021	2022
106	323	269	427	475	463
66	284	302	160	524	225
111	256	327	382	611	0
72	369	340	221	516	0
9	6	69	64	0	0
44	31	299	189	202	0
140	245	472	618	665	0
117	150	301	556	606	0
76	261	144	469	486	0
88	332	330	458	474	0
211	244	226	455	458	0
191	190	292	508	459	0